Positioning Accuracy Improvement of Automated Guided Vehicles Based on a Novel Magnetic Tracking Approach

Automated guided vehicles (AGVs) have been widely adopted in the logistic delivering of modern manufacturing. As a key performance index for an AGV, the positioning accuracy of commercial AGVs based on the traditional magnetic tracking approach is bigger than ?5mm, which cannot meet the requirement of many industrial applications. Thus, we proposed a novel magnetic tracking approach to improve the positioning accuracy of AGVs. A super strong magnetic nail, instead of the low-remanence magnetic nail, can be more easily tracked by a two-dimensional (2D) rather than 1 D sensor array. The magnetic flux intensity around the magnetic nail can be expressed as a dipole model. Hence, the location and orientation of the nail can be computed via the sensor array data and a hybrid optimization algorithm, which is combined by the particle swarm optimization (PSO) algorithm and Levenberg-Marquardt (LM) algorithm performed on a microcontroller. We carried out experiments to verify the performance of the proposed positioning system in a series of initial driving speeds and target distances, where an N35 neodymium magnetic nail functioned as the designated AGV positioning point. Results show that the average positioning accuracy is improved to ?1.69mm, and the positioning accuracy can be further improved by a better motion control strategy. In addition, our proposed magnetic tracking approach can be easily fused with other navigation approaches such as laser and inertial sensing.